Selective personalization using topical user profile to improve search results

Personalization is a technique used in Web search engines to improve the effectiveness of information retrieval systems. In the field of personalized web search has recently been doing a lot of research and applications. In this research, we evaluate the effect of personalization for queries with different characteristics. With this analysis, the question of whether personalization should be applied for all queries in the same way or not is investigated. While personalizing some queries yields significant improvements on user experience by providing a ranking inline with the user preferences, it fails to improve or even degrades the effectiveness for less ambiguous queries. A potential for personalization metric can improve search engines by selectively applying a personalization. Current methods for estimating the potential for personalization such as click entropy and topic entropy are based on the clicked document for query or query history. They have limitations like unavailability of the prior clicked data for new and unseen queries or queries without history. In this thesis, the topic entropy measure is improved by integrating the user distribution to the metric, robust to the sparsity problem. This metric estimates the potential for personalization using a topical user profile created on user documents. In this way, we can overcome the cold start problem to estimate the potential for new queries and increase the accuracy of estimates for queries with history. Although in this thesis the main focus is on topic-based user profiles, since there is not more research on keyphrase-based user profiles in the process of personalization, we do a comparison research between keyphrase-based and topic-based profiles. We examine how personalization can be integrated into the state of the art keyphrase extraction models by considering different models of supervised and unsupervised methods. We evaluate topicbased and keyphrase-based user profiles using a re-ranking algorithm to complete the process of personalization using different datasets. In personalization using keyphrase-based profiles, personalized models based on supervised keyphrase extraction approaches obtained more accuracy by 7% than unsupervised approaches however it does not improve compared to topic-based models. In topic-based models, we use a combination of personalization in the level of user-specified and group profiling as part of the ranking process. In the previous ranking methods, more improvement in ranking is for the queries which match the user's history. To take advantage of ranking for all queries, we present a group personalized topical model(GPTM) that uses groups obtained from clustered similar users on topical profiles. Experiments reveal that the proposed potential prediction method correlates with human query ambiguity judgments and group profiles based ranking method improve the Mean Reciprocal Rank by 8%.